Method and apparatus for digital image segmentation using an iterative method

ABSTRACT

A digital image is divided into segments, wherein the digital image comprises an array of pixels each having a pixel location and a pixel color value. A method comprises obtaining an image frame comprising an array of pixels each having a pixel color value, assigning an initial segment identifier to each pixel in the image frame independent of each pixel&#39;s pixel color value, testing, using an appropriateness test, pixels for possible reassignment from a current segment to a neighboring segment, and if the appropriateness test indicates a pixel should be reassigned, reassigning the segment identifier of the pixel.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present disclosure is related to the following:

U.S. Pat. No. 6,600,786 [U.S. patent application Ser. No. 09/550,705,filed Apr. 17, 2000 and entitled “Method and Apparatus for EfficientVideo Processing”] (hereinafter “Prakash I”).

U.S. Pat. No. 6,778,698 [U.S. patent application Ser. No. 09/591,438,filed Jun. 9, 2000 and entitled “Method and Apparatus for Digital ImageSegmentation”] (hereinafter “Prakash II”).

The present disclosure claims priority from:

U.S. Provisional Patent Application No. 60/222,834, filed Aug. 4, 2000and entitled “Grid-Based Segmentation.”

The disclosures of each of the above are hereby incorporated byreference for all purposes.

BACKGROUND OF THE INVENTION

Image segmentation is the process of partitioning an image into a set ofnon-overlapping parts, or segments, that together constitute the entireimage. Image segmentation is useful for many applications, one of whichis compression. For example, it is well known that if an image made upof pixels with the image represented by pixel color values for each ofthe pixels, the image representation can be compressed if contexts forthe pixels can be extracted and used in representing the pixel colorvalues. Thus, if a pixel color value can be one of 256 color values, itcan be represented by eight bits, but if a pixel color value is morelikely to be a given color value in view of surrounding color values(e.g., in many images, a pixel surrounded by a field of blue pixels islikely to be a blue pixel), then those pixel color values can berepresented, on average, in fewer than eight bits. Compression resultsare likely to be improved if the pixels used for context are actuallythe context of the pixel being compressed and not pixels that areindependent of the pixel being compressed.

Segmentation is helpful in determining which pixels are likely to berelated enough to other pixels such that related pixels are used forcontext with more weight than unrelated pixels. While segmentation isuseful for compression, segmentation is also useful for otherapplications, some of which are mentioned here. For example, a computerprogram that is not doing compression might process each segment of animage in a manner specific to that segment or associate different data,such as labels, with different segments. In any case, correctlysegmenting the image is important for proper operation of the processbeing performed on the image.

As the terms are used herein, an image is an array of pixel color valuessuch that pixels placed in an array and colored according to eachpixel's color value would represent the image. Each pixel has a locationand can be thought of as being a point at that location or as a shapethat fills the area around the pixel such that any point within theimage is considered to be “in” a pixel's area or considered to be partof the pixel. The image itself might be a multidimensional pixel arrayon a display, on a printed page, an array stored in memory, or a datasignal being transmitted and representing the image. Themultidimensional pixel array can be a two-dimensional array for atwo-dimensional image, a three-dimensional array for a three-dimensionalimage, or some other number of dimensions.

The image can be an image of a physical space or plane or an image of asimulated and/or computer-generated space or plane. In the computergraphic arts, a common image is a two-dimensional view of acomputer-generated three-dimensional space (such as a geometric model ofobjects and light sources in a three-space). An image can be a singleimage or one of a plurality of images that, when arranged in a suitabletime order, form a moving image, herein referred to as a video sequence.

When an image is segmented, the image is represented by a plurality ofsegments. The degenerate case of a single segment comprising the entireimage is within the definition of segment used here, but the typicalsegmentation divides an image into at least two segments. In manyimages, the segmentation divides the image into a background segment andone or more foreground segments.

In one segmentation method, an image is segmented such that each segmentrepresents a region of the image where the pixel color values are moreor less uniform within the segment, but dramatically change at the edgesof the image. In that implementation, the regions are connected, i.e.,it is possible to move pixel-by-pixel from any one pixel in the regionto any other pixel in the region without going outside the region.

Pixel color values can be selected from any number of pixel colorspaces. One color space in common use is known as the YUV color space,wherein a pixel color value is described by the triple (Y, U, V), wherethe Y component refers to a grayscale intensity or luminance, and U andV refer to two chrominance components. The YUV color space is commonlyseen in television applications. Another common color space is referredto as the RGB color space, wherein R, G and B refer to the Red, Greenand Blue color components, respectively. The RGB color space is commonlyseen in computer graphics representations, along with CYMB (cyan,yellow, magenta, black) often used with computer printers.

An example of image segmentation is illustrated in FIG. 1. There, animage frame 10 contains an oval 12, a rectangle 14 and a triangle 16 ona background 18. The image can be segmented into segments based oncolors (the shading of the oval 12 in FIG. 1 represents a color distinctfrom the colors of the rectangle 14, the triangle 16 or the background18). Thus, the oval 12, the rectangle 14, the triangle 16 and thebackground 18 are segmented into separate segments in this example.

In this example, if each segment has a very distinct color and theobjects in the image 10 end cleanly at pixel boundaries, segmentation isa simple process. In general, however, generating accurate imagesegments is a difficult problem and there is much open research on thisproblem, such as in the field of “computer vision” research. One reasonsegmentation is often difficult is that a typical image includes noiseintroduced from various sources including, but not limited to, thedigitization process when the image is captured by physical devices andthe image also includes regions that do not have well-definedboundaries.

There are several ways of approaching the task of image segmentation,which can generally be grouped into the following: 1) histogram-basedsegmentation; 2) traditional edge-based segmentation; 3) region-basedsegmentation; and 4) hybrid segmentation, in which several of the otherapproaches are combined. Each of these approaches is described below.

1. Histogram-Based Segmentation

Segmentation based upon a histogram technique relies on thedetermination of the color distribution in each segment. This techniqueuses only one color plane of the image, typically an intensity colorplane (also referred to as the greyscale portion of the image), forsegmentation. To perform the technique a processor creates a histogramof the pixel color values in that plane. A histogram is a graph with aseries of “intervals” each representing a range of values arrayed alongone axis and the total number of occurrences of the values within eachrange shown along the other axis. The histogram can be used to determinethe number of pixels in each segment, by assuming that the colordistribution within each segment will be roughly a Gaussian, orbell-shaped, distribution and the color distribution for the entireimage will be a sum of Gaussian distributions. Histogram-basedtechniques attempt to recover the individual Gaussian curves by varyingthe size of the intervals, i.e., increasing or decreasing the valuerange, and looking for high or low points. Once the distributions havebeen ascertained, the each pixel is assigned to the segment with itscorresponding intensity range.

The histogram method is fraught with errors. The fundamental assumptionthat the color distribution is Gaussian is at best a guess, which maynot be accurate for all images. In addition, two separate regions ofidentical intensity will be considered the same segment. Further, theGaussian distributions recovered by the histogram are incomplete in thatthey cut off at the ends, thus eliminating some pixels. Further, thismethod of segmentation is only semi-automatic, in that the techniquerequires that the number of segments are previously known and that allof the segments are all roughly the same size.

2. Traditional Edge-Based Segmentation

Traditional edge-based segmentation uses differences in color orgreyscale intensities to determine edge pixels that delineate variousregions within an image. This approach typically assumes that when edgepixels are identified, the edge pixels will completely enclose distinctregions within the image, thereby indicating the segments. However,traditional edge detection techniques often fail to identify all thepixels that are in fact edge pixels, due to noise in images or otherartifacts. If some edge pixels are missed, some plurality of distinctregions might be misidentified as being a single segment.

3. Region-Based Segmentation

Region based segmentation attempts to detect homogenous regions anddesignate them as segments. One class of region-based approaches startswith small uniform regions within the image and tries to mergeneighboring regions that are of very close color value in order to formlarger regions. Conversely, another class of region-based approachesstarts with the entire image and attempts to split the image intomultiple homogeneous regions. Both of these approaches result in theimage being split at regions where some homogeneity requirements are notmet.

The first class of region based segmentation approaches is limited inthat the segment edges are approximated depending on the method ofdividing the original image. A problem with the second class of regionbased approaches is that the segments created tend to be distortedrelative to the actual underlying segments.

4. Hybrid Segmentation

The goal of hybrid techniques is to combine processes from multipleprevious segmentation processes to improve image segmentation. Mosthybrid techniques are a combination of edge segmentation andregion-based segmentation, with the image being segmented using one ofthe processes and being continued with the other process. The hybridtechniques attempt to generate better segmentation than a single processalone. However, hybrid methods have proven to require significant userguidance and prior knowledge of the image to be segmented, thus makingthen unsuitable for applications requiring fully automated segmentation.

SUMMARY OF THE INVENTION

The present invention provides an exemplary method and apparatus fordigital image segmentation. For example, in one embodiment, a pluralityof segments are created from a digital image frame using grid-basedsegmentation.

Accordingly, in one embodiment, a digital image comprised of an array ofpixels each having a pixel location and a pixel color value, is dividedinto regularly ordered segments. A method comprises obtaining an imageframe, assigning an initial segment identifier to each pixel in theimage frame independent of each pixel's color value, testing, using anappropriateness test, pixels for possible reassignment from a currentsegment to a neighboring segment, and if the appropriateness testindicates a pixel should be reassigned, reassigning the segmentidentifier of the pixel.

In another embodiment, a digital image is divided into segments, whereinthe digital image comprises an array of pixels each having a pixellocation and a pixel color value. A method comprises obtaining an imageframe comprising an array of pixels each having a pixel color value,overlaying a logical grid over the image data such that initially eachsubsection of the grid encompasses at least two pixels, and adjustingthe grid subsection boundaries to create at least one segment.

A further understanding of the nature and advantages of the inventionsherein may be realized by reference to the remaining portions of thespecification and the attached drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of a digital image segmentation.

FIG. 2 illustrates an example of an overlaid grid.

FIG. 3 illustrates an example of a partially adjusted overlaid grid.

FIG. 4 illustrates an example of an adjusted overlaid grid.

FIG. 5 is an image frame with an overlaid grid.

FIG. 6 illustrates two subsections and how average pixel values can becomputed.

FIG. 7A illustrates border pixels which are to be considered forreassignment.

FIG. 7B illustrates a logical grid being changed in shape.

FIG. 8A illustrates a border pixel of intermediate value

FIG. 8B illustrates a logical grid being changed in shape due toinclusion of a border pixel of intermediate value

FIG. 8C illustrates a logical grid remaining unchanged due to the valueof the border pixel

FIG. 9A illustrates an outlier problem.

FIG. 9B illustrates one outlier pixel being identified.

FIG. 9C illustrates an outlier pixel in an adjacent segment beingidentified.

FIG. 10 illustrates a resulting segment identifier map.

DETAILED DESCRIPTION OF THE EMBODIMENTS

With reference to the exemplary drawings wherein like reference numeralsindicate like or corresponding elements among the figures, embodimentsof a system according to the present invention will now be described indetail. In one aspect, segmentation is used as an integral portion of avideo delivery platform fully described in Prakash I and Prakash II.

Referring to FIGS. 2-4, one embodiment of a grid-based segmentationprocess is illustrated. A method and apparatus are provided for thecreation of segments from a digital image. The method includes obtaininga digital image comprising an array of pixels, each pixel beingdescribed by a location, a color value and an initial segment identifier(i.e., something that indicates to which segment each pixel belongs).

A given digital frame is first divided into a grid of segmentsindependent of the image contents. In one embodiment, the grid comprisesa plurality of rectangles. For purposes of illustration, 16 rectanglesare shown in FIG. 2; however, in practice the frame may be divided intoany number of rectangles. For example, the frame may be divided into2000 rectangles. A process is then applied, as described in more detailbelow, to adjust the boundaries of the grid rectangles based on theunderlying pixel values.

FIG. 3 shows a partial adjustment of the boundaries and FIG. 4 shows afinal adjustment of the boundaries such that the boundaries definesegments and pixels within a given segment have substantially uniformcolor values. A segmentation mask is thus produced. It should be notedthat a segmentation mask, also referred to as a logical mask or simply amask for the purposes of this patent, can be of the shape of the initialgrid or it can take on various different shapes. In one embodiment, therectangles take on amorphous shapes by adjusting pixels on theboundaries one at a time, as will be described below.

FIG. 5 illustrates a digital image comprising white and black imagepixels. In one specific embodiment, white pixels have a value of 255,while black pixels have a value of 0. It should be noted thatembodiments according to the present invention are not limited to blackand white pixels, but may relate to pixels of varying colors.

A logical mask 51 is overlaid on the image. The logical mask divides theimage frame into at least one image region and likely a plurality ofimage regions, the sum of which equals the original image frame.

In one embodiment, the boundaries of the logical mask exist between theimage pixels. In another embodiment of the invention, some maskboundaries intersect pixels. The logical mask can be a grid or take onother arrangements. Within each subsection of the grid preferably atleast two image pixels are initially contained. Each subsection isassigned a unique identifier, shown in the upper left corner of eachsubsection. Since each pixel is within a given subsection, each pixelhas associated with it one of the unique identifiers. In one embodiment,the unique identifier is represented numerically. In embodiments wheresubsection boundaries can intersect pixels, those intersected pixels areassigned to one subsection according to a predefined rule. For example,a pixel might be assigned to the leftmost subsection it is at leastpartially in and to the upper subsection it is in when the pixel isintersected by a horizontal boundary.

FIG. 6 illustrates how pixels are reassigned to different subsections.The illustration relates to two adjacent subsections, namely subsections60 and 61. Subsection 60 comprises nine pixels, each having a pixelvalue of 255 (show as white pixels). Subsection 61 comprises two pixelshaving pixel values of 0 and seven having pixel values of 255 (shown asblack pixels and white pixels, respectively).

In one embodiment, an image processor computes a representative value(median, average, etc.) of the pixel values in subsection 60. Thisaverage value is 255. Similarly, the average pixel value of subsection61 is computed to be approximately 198. In this case, the black pixels(each having a value of 0) are not reassigned because 198 is closer to 0than is 255.

In another embodiment, the image processor takes a representative valueof the pixel values in subsections 60 and 61, which are sufficientlyproximate to a border pixel to be considered.

FIG. 7A illustrates border pixels which are to be considered foridentifier reassignment. The white border pixels marked 73 each have avalue of 255, which is closer to the average pixel value of subsection70 than the average pixel value of subsection 71. Similarly, the blackborder pixels marked 74 each have a value of zero, which is closer tothe average pixel value of subsection 71 than subsection 70. Thus pixels73 and 74 retain their original subsection identifiers. However, pixel75, a white pixel in subsection 71, has a value of 255, which is closerto the average values in subsection 70. Therefore, the segmentidentifier of pixel 75 is changed to reflect inclusion in subsection 70.

FIG. 7B illustrates the new shape of the logical mask. Pixel 75 is nowwithin grid 70. This is repeated until the process converges, i.e.,until the boundaries do not change in an adjustment pass over the imageanymore or the boundaries change less than a threshold amount. Thesesubsections are now known as segments.

FIGS. 8A, B and C illustrate the method assigning segment identifiers topixels that are intermediate in value. One embodiment of a gray pixel isillustrated in FIG. 8A where, pixel 82 has a value closer to the averagevalue of the pixels in subsection 80 than in subsection 81. Hence, asillustrated in FIG. 8B, pixel 82 is assigned to subsection 80 and theshape of subsection 80 is changed as result of the inclusion of pixel82. In another embodiment, the gray pixel 85 in FIG. 8C, has a valuecloser to the average value of subsection 84 than that of subsection 83.Hence the segment identifier of pixel 85 is not changed and pixel 85remains a part of subsection 84.

FIG. 9A illustrates the outlier problem. In this case Pixel 93 has avalue close to the average pixel value of segment 92, while Pixel 94 hasa value close to the average pixel value of segment 91. In this casepixel 93 is temporarily assigned to subsection 92 as depicted in FIG.9B. At the same time, pixel 94 is temporarily assigned to subsection 91as depicted in FIG. 9C. It is important to note that this the segmentassignment illustrated in FIGS. 9B and 9C is only temporary and theyoccur simultaneously.

In one embodiment, an outlier pass is performed subsequent to thetemporary segment assignment depicted in FIGS. 9B and C. This processchecks for segment boundaries that are not continuous. This is preciselythe case that arises in segment 91 and segment 92 where pixel 93temporarily belongs to subsection 92 although it is not connected tosegment 92 and pixel 94 temporarily belongs to subsection 91 although itis not connected to segment 91. Such disconnected segments are notallowable. This step corrects the situation of disconnected segments byreassigning the segment boundaries and returning pixel 93 to segment 91and pixel 94 to segment 92. Thus the segment assignment illustrated inFIG. 9A is restored.

FIG. 10 illustrates the resulting segment identifier map from theoriginal image. All of the black pixels are together with a certainsegment identifier. The white pixels are in a plurality of subsections.Any outliers have been changed appropriately. In one embodiment, theinvention employs a gluing pass to reduce the number of segments bygluing segments of similar pixel value characteristics together.

The above description is illustrative and not restrictive. The scope ofthe invention should, therefore, be determined not with reference to theabove description, but instead should be determined with reference tothe appended claims along with their full scope of equivalents.

What is claimed is:
 1. A method of dividing a digital image intosegments, wherein the digital image comprises an array of pixels eachhaving a pixel location and a pixel color value, the method comprising:obtaining an image frame comprising an array of pixels each having apixel color value; assigning an initial segment identifier to each pixelin the image frame independent of each pixel's pixel color value;testing, using an appropriateness test, pixels for possible reassignmentfrom a current segment to a neighboring segment; and if theappropriateness test indicates a pixel should be reassigned, reassigningthe segment identifier of the pixel.
 2. The method of claim 1, furthercomprising reassigning the pixel color of outlier pixels to therepresentative color of the immediately adjacent pixels.
 3. The methodof claim 1, wherein the pixel segment identifier is based upon thepixel's location with a logical grid, the logical grid having a uniquesegment identifier corresponding initially to a logical grid section. 4.The method of claim 1, wherein reassigning comprises: obtaining aboundary pixel, wherein a boundary pixel is a pixel located proximate toa pixel having a different segment identifier than that of the boundarypixel; considering pixel values in a neighborhood adjacent to theboundary pixel; logically grouping the neighboring pixels by commonalityof segment identifiers; computing a representative color value for eachpixel group; and reassigning the segment identifier of the boundarypixel to match the group whose representative color value most closelymatches the color of the boundary pixel.
 5. The method of claim 1,further comprising repeating the steps of testing and reassigning untilthe segment boundaries converge such that the testing step results in nopossible reassignments.
 6. The method of claim 1, further comprisingrepeating the steps of testing and reassigning until the segmentboundaries converge such that the testing step results in less than athreshold amount of reassignment.
 7. The method of claim 6, wherein thethreshold amount of reassignment is measured as a predetermined numberof pixels.
 8. A method of dividing a digital image into segments,wherein the digital image comprises an array of pixels each having apixel location and a pixel color value, the method comprising: obtainingan image frame comprising an array of pixels each having a pixel colorvalue; overlaying a logical grid over the image data such that initiallyeach subsection of the grid encompasses at least two pixels; andadjusting the grid subsection boundaries to create at least one segment.9. The method of claim 8, wherein moving further comprises the steps of:obtaining a boundary pixel; considering pixel values in the gridsubsections adjacent to and including the boundary pixel; computing arepresentative pixel color value for each considered grid subsection;and adjusting the grid subsection boundaries such that the consideredboundary pixel is associated with the grid subsection with the closestcolor value to the boundary pixel's color value.
 10. The method of claim8, the method further comprising reassigning the pixel color of outlierpixels to a representative color of immediately adjacent pixels.
 11. Themethod of claim 8, wherein considering the pixels comprises consideringonly the pixels within a given neighborhood of the boundary pixel.